class_docstr: Represents structured linear-chain CRFs, generalizing HMMs smoothing, tagging models,
  and anything with chain-like dynamics.
code: def _rand(min_n=2):
      b = torch.randint(2, 4, (1,))
      N = torch.randint(min_n, 4, (1,))
      C = torch.randint(2, 4, (1,))
      return torch.rand(b, N, C, C), (b.item(), (N + 1).item())
  
file_path: pytorch-struct/torch_struct/linearchain.py
imports: import torch
  from .helpers import _Struct
label_desc: Use image node_id 6 for calling the LinearChain obj's underlying member method code with example usage: obj._rand(min_n) and returns: torch,  b,  unknown